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pipeline.py

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+
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from diffusers import StableDiffusionPipeline
+import torch
+
+# Load models
+t5_model = T5ForConditionalGeneration.from_pretrained('t5_model')
+t5_tokenizer = T5Tokenizer.from_pretrained('t5_tokenizer')
+ArtifyAI_model = StableDiffusionPipeline.from_pretrained('ArtifyAI_model', torch_dtype=torch.float16)
+ArtifyAI_model = ArtifyAI_model.to('cuda')
+
+# Combined pipeline
+def t5_to_image_pipeline(input_text):
+    # T5 model processing
+    t5_inputs = t5_tokenizer.encode(input_text, return_tensors='pt', truncation=True)
+    summary_ids = t5_model.generate(t5_inputs, max_length=50, num_beams=5, early_stopping=True)
+    generated_text = t5_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    
+    # Generate image from text using Stable Diffusion
+    image = ArtifyAI_model(generated_text).images[0]
+    return image